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OBJECTIVE: Investigating doctors' communicative practices for recommending surgery to amputees when the proposal counters patients' expectation. METHOD: Conversation Analysis of 77 videorecorded medical consultations at an Italian prosthesis clinic. RESULTS: Compared to the direct format doctors used to prescribe prosthesis, when suggesting surgery doctors adopted a more circuitous, indirect approach. They used a range of communication strategies, orientating to patients' likely resistance - indeed, patients were frequently observed to reject surgical options. CONCLUSIONS: Considering patients' expectations is part of a patient centred approach, hence the cautious ways in which doctors introduce the option of surgery. Moreover, doctors do not pursue recommending surgery when patients display their reluctance or resistance. PRACTICE IMPLICATIONS: Doctors in prosthetics clinics might adopt a more balanced communicative strategy that takes into account patients' perspectives, concerns and expectations, whilst but also providing patients with the necessary information to collaborate meaningfully to decision making.

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Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision-making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision-making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision-making tools are data security, data representation, and explainability of AI-based outcome predictions, in particular for decision-making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.

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Biomedical information retrieval for diagnosis, treatment and prognosis has been studied for a long time. In particular, image recognition using deep learning has been shown to be very effective for cancers and diseases. In these fields, scaphoid fracture recognition is a hot topic because the appearance of scaphoid fractures is not easy to detect. Although there have been a number of recent studies on this topic, no studies focused their attention on surgical treatment recommendations and nonsurgical prognosis status classification. Indeed, a successful treatment recommendation will assist the doctor in selecting an effective treatment, and the prognosis status classification will help a radiologist recognize the image more efficiently. For these purposes, in this paper, we propose potential solutions through a comprehensive empirical study assessing the effectiveness of recent deep learning techniques on surgical treatment recommendation and nonsurgical prognosis status classification. In the proposed system, the scaphoid is firstly segmented from an unknown X-ray image. Next, for surgical treatment recommendation, the fractures are further filtered and recognized. According to the recognition result, the surgical treatment recommendation is generated. Finally, even without sufficient fracture information, the doctor can still make an effective decision to opt for surgery or not. Moreover, for nonsurgical patients, the current prognosis status of avascular necrosis, non-union and union can be classified. The related experimental results made using a real dataset reveal that the surgical treatment recommendation reached 80% and 86% in accuracy and AUC (Area Under the Curve), respectively, while the nonsurgical prognosis status classification reached 91% and 96%, respectively. Further, the methods using transfer learning and data augmentation can bring out obvious improvements, which, on average, reached 21.9%, 28.9% and 5.6%, 7.8% for surgical treatment recommendations and nonsurgical prognosis image classification, respectively. Based on the experimental results, the recommended methods in this paper are DenseNet169 and ResNet50 for surgical treatment recommendation and nonsurgical prognosis status classification, respectively. We believe that this paper can provide an important reference for future research on surgical treatment recommendation and nonsurgical prognosis classification for scaphoid fractures.

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Background: There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients. Aim: This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection. Methods: We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation. Results: The BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40-7.39; hazard ratio (HR): 0.71; 95% CI, 0.65-0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group. Conclusion: The ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

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Introduction: Intrauterine adhesions (IUAs) caused by endometrial injury, commonly occurring in developing countries, can lead to subfertility. This study aimed to develop and evaluate a DeepSurv architecture-based artificial intelligence (AI) system for predicting fertility outcomes after hysteroscopic adhesiolysis. Methods: This diagnostic study included 555 intrauterine adhesions (IUAs) treated with hysteroscopic adhesiolysis with 4,922 second-look hysteroscopic images from a prospective clinical database (IUADB, NCT05381376) with a minimum of 2 years of follow-up. These patients were randomly divided into training, validation, and test groups for model development, tuning, and external validation. Four transfer learning models were built using the DeepSurv architecture and a code-free AI application for pregnancy prediction was also developed. The primary outcome was the model's ability to predict pregnancy within a year after adhesiolysis. Secondary outcomes were model performance which evaluated using time-dependent area under the curves (AUCs) and C-index, and ART benefits evaluated by hazard ratio (HR) among different risk groups. Results: External validation revealed that using the DeepSurv architecture, InceptionV3+ DeepSurv, InceptionResNetV2+ DeepSurv, and ResNet50+ DeepSurv achieved AUCs of 0.94, 0.95, and 0.93, respectively, for one-year pregnancy prediction, outperforming other models and clinical score systems. A code-free AI application was developed to identify candidates for ART. Patients with lower natural conception probability indicated by the application had a higher ART benefit hazard ratio (HR) of 3.13 (95% CI: 1.22-8.02, p = 0.017). Conclusion: InceptionV3+ DeepSurv, InceptionResNetV2+ DeepSurv, and ResNet50+ DeepSurv show potential in predicting the fertility outcomes of IUAs after hysteroscopic adhesiolysis. The code-free AI application based on the DeepSurv architecture facilitates personalized therapy following hysteroscopic adhesiolysis.

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BACKGROUND: Gestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes. METHODS: We conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC. RESULTS: GDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24 weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations. CONCLUSIONS: The GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/ .

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Background: While substantial progress has been made in the development of disease-modifying medications for multiple sclerosis (MS), a high percentage of treated patients still show progression and persistent inflammatory activity. Autologous haematopoietic stem cell transplantation (AHSCT) aims at eliminating a pathogenic immune repertoire through intense short-term immunosuppression that enables subsequent regeneration of a new and healthy immune system to re-establish immune tolerance for a long period of time. A number of mostly open-label, uncontrolled studies conducted over the past 20 years collected about 4000 cases. They uniformly reported high efficacy of AHSCT in controlling MS inflammatory disease activity, more markedly beneficial in relapsing-remitting MS. Immunological studies provided evidence for qualitative immune resetting following AHSCT. These data and improved safety profiles of transplantation procedures spurred interest in using AHSCT as a treatment option for MS. Objective: To develop expert consensus recommendations on AHSCT in Germany and outline a registry study project. Methods: An open call among MS neurologists as well as among experts in stem cell transplantation in Germany started in December 2021 to join a series of virtual meetings. Results: We provide a consensus-based opinion paper authored by 25 experts on the up-to-date optimal use of AHSCT in managing MS based on the Swiss criteria. Current data indicate that patients who are most likely to benefit from AHSCT have relapsing-remitting MS and are young, ambulatory and have high disease activity. Treatment data with AHSCT will be collected within the German REgistry Cohort of autologous haematopoietic stem CeLl trAnsplantation In MS (RECLAIM). Conclusion: Further clinical trials, including registry-based analyses, are urgently needed to better define the patient characteristics, efficacy and safety profile of AHSCT compared with other high-efficacy therapies and to optimally position it as a treatment option in different MS disease stages.

Autologous haematopoietic stem cell transplantation for multiple sclerosis Substantial progress has been made in the development of disease-modifying medications for multiple sclerosis (MS) during the last 20 years. However, in a relevant percentage of patients, the disease cannot completely be contained. Autologous haematopoietic stem cell transplantation (AHSCT) enables rebuilding of a new and healthy immune system and to potentially stop the autoimmune disease process for a long time. A number of studies documenting 4000 cases cumulatively over the past 20 years reported high efficacy of AHSCT in controlling MS inflammatory disease activity. These data and improved safety profiles of the treatment procedures spurred interest in using AHSCT as a treatment option for MS. An open call among MS neurologists as well as among experts in stem cell transplantation in Germany started in December 2021 to join a series of video calls to develop recommendations and outline a registry study project. We provide a consensus-based opinion paper authored by 25 experts on the up-to-date optimal use of AHSCT in managing MS. Current data indicate that patients are most likely to benefit from AHSCT if they are young, ambulatory, with high disease activity, that is, relapses or new magnetic resonance imaging (MRI) lesions. Treatment data with AHSCT will be collected within the German REgistry Cohort of autoLogous haematopoietic stem cell transplantation MS (RECLAIM). Further clinical trials including registry-based analyses and systematic follow-up are urgently needed to better define the optimal patient characteristics as well as the efficacy and safety profile of AHSCT compared with other high-efficacy therapies. These will help to position AHSCT as a treatment option in different MS disease stages.

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Tebentafusp is a new T cell receptor bispecific fusion protein and the first approved treatment option for human leucocyte antigen-A*02:01 (HLA-A*02:01) metastatic uveal melanoma, with a proven benefit in overall survival versus the investigator's choice. As a first-in-class therapeutic option, this Immune mobilising monoclonal T cell receptor Against Cancer (ImmTAC) is associated with a new adverse event (AE) profile. Based on clinical experience, a national expert group discussed recommendations for tebentafusp treatment, focusing on AE management. Further topics included prerequisites for initiating tebentafusp treatment, appropriate treatment setting, and patient selection criteria. To provide guidance for treating physicians, the resulting recommendations are summarised including a model standard operating procedure for AE management. Patients in good clinical condition and with a low tumour burden are good candidates for tebentafusp treatment, particularly if treated as early as possible after the diagnosis of metastatic disease. The safety profile of tebentafusp is manageable and includes two major pathologies: cytokine release syndrome (CRS) and skin-related events. Postdose monitoring should thus focus on pyrexia and hypotension as the first symptoms of cytokine release. To minimise the risk of hypotension associated with CRS, patients should receive intravenous fluids before starting treatment. The monitoring of liver values is crucial, as patients may experience an increase in transaminases, which can even manifest as tumour lysis syndrome.

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The diagnostic performance of the SYNTAX score 2020 (SS-2020) when calculated using CCTA remains unknown. This study aimed to compare treatment recommendations based on the SS-2020 derived from coronary computed tomography angiography (CCTA) versus invasive coronary angiography (ICA). This interim analysis included 57 of the planned 114 patients with de-novo three-vessel disease, with or without left main coronary artery disease, enrolled in the ongoing FASTTRACK CABG trial. The anatomical SYNTAX scores derived from ICA or CCTA were evaluated by two separate teams of blinded core-lab analysts. Treatment recommendations were based on a maximal individual absolute risk difference in all-cause mortality between percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG) of 4.5% ([predicted PCI mortality] - [predicted CABG mortality]). The level of agreement was evaluated with Bland-Altman plots and Cohen's Kappa. The mean age was 66.2 ± 9.2 years and 89.5% of patients were male. Mean anatomical SYNTAX scores derived from ICA and CCTA were 35.1 ± 11.5 and 35.6 ± 11.4 (p = 0.751), respectively. The Bland-Altman analysis showed mean differences of - 0.26 and - 0.93, with standard deviation of 3.69 and 5.23, for 5- and 10-year all-cause mortality, respectively. The concordance in recommended treatment for 5- and 10-year mortalities were 84.2% (48/57 patients) and 80.7% (46/57 patients), with Cohen's κ coefficients of 0.672 and 0.551. There was moderate to substantial agreement between treatment recommendations based on the SS-2020 derived using CCTA and ICA, suggesting that CCTA could be used as an alternative to ICA when making decisions regarding the modality of revascularization.

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This study evaluated the reliability and comprehensiveness of the Unified classification system (UCPF), Wright & Cofield, Worland and Kirchhoff classifications and related treatment recommendations for periprosthetic shoulder fractures (PPSFx). Two shoulder arthroplasty specialists (experts) and two orthopaedic residents (non-experts) assessed 20 humeral-sided and five scapula-sided cases of PPSFx. We used the unweighted Cohen's Kappa (κ) for measuring the intra-observer reliability and Krippendorff's alpha (α) for measuring the inter-observer reliability. The inter-rater reliabilities for the Wright & Cofield and Worland classifications were substantial for all groups. The expert and non-expert groups for UCPF also showed substantial inter-rater agreement. The all-rater group for the UCPF and the expert and non-expert group for the Kirchhoff classification revealed moderate inter-rater reliability. For the Kirchhoff classification, only fair inter-rater reliability was found for the non-expert group. Almost perfect intra-rater reliability was measured for all groups of the Wright & Cofield classification and the all-rater and expert groups of the UCPF. All groups of the Kirchhoff and Worland classifications and the group of non-experts for the UCPF had substantial intra-rater reliabilities. Regarding treatment recommendations, substantial inter-rater and moderate intra-rater reliabilities were found. Simple classification systems for PPSFx (Wright & Cofield, Worland) show the highest inter- and intra-observer reliability but lack comprehensiveness as they fail to describe scapula-sided fractures. The complex Kirchhoff classification shows limited reliability. The UCPF seems to offer an acceptable combination of comprehensiveness and reliability.

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Objective: To compare the performance of three machine learning algorithms with the tumor, node, and metastasis (TNM) staging system in survival prediction and validate the individual adjuvant treatment recommendations plan based on the optimal model. Methods: In this study, we trained three machine learning madel and validated 3 machine learning survival models-deep learning neural network, random forest and cox proportional hazard model- using the data of patients with stage-al3 NSCLC patients who received resection surgery from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database from 2012 to 2017,the performance of survival predication from all machine learning models were assessed using a concordance index (c-index) and the averaged c-index is utilized for cross-validation. The optimal model was externally validated in an independent cohort from Shaanxi Provincial People's Hospital. Then we compare the performance of the optimal model and TNM staging system. Finally, we developed a Cloud-based recommendation system for adjuvant therapy to visualize survival curve of each treatment plan and deployed on the internet. Results: A total of 4617 patients were included in this study. The deep learning network performed more stably and accurately in predicting stage-iii NSCLC resected patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.834 vs. 0.678 vs. 0.640) and better than TNM staging system (C-index=0.820 vs. 0.650) in the external validation. The individual patient who follow the reference from recommendation system had superior survival compared to those who did not. The predicted 5-year-survival curve for each adjuvant treatment plan could be accessed in the recommender system via the browser. Conclusion: Deep learning model has several advantages over linear model and random forest model in prognostic predication and treatment recommendations. This novel analytical approach may provide accurate predication on individual survival and treatment recommendations for resected Stage-iii NSCLC patients.

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Treatment recommendation, as a critical task of delivering effective interventions according to patient state and expected outcome, plays a vital role in precision medicine and healthcare management. As a well-suited tactic to learn optimal policies of recommender systems, reinforcement learning is promising to address the challenge of treatment recommendation. However, existing solutions mostly require frequent interactions between treatment recommender systems and clinical environment, which are expensive, time-consuming, and even infeasible in clinical practice. In this study, we present a novel model-based offline reinforcement learning approach to optimize a treatment policy by utilizing patient treatment trajectories in Electronic Health Records (EHRs). Specifically, a patient treatment trajectory simulator is firstly constructed based on the ground-truth trajectories in EHRs. Thereafter, the constructed simulator is utilized to model the online interactions between the treatment recommender system and clinical environment. In this way, the counterfactual trajectories can be generated. To alleviate the bias deriving from the ground-truth and the counterfactual trajectories, an adversarial network is incorporated into the proposed model, such that a large space of treatment actions can be explored with the scaled rewards. The proposed model is evaluated on a simulated dataset and a real-world dataset. The experimental results demonstrate that the proposed model is superior to other methods, giving rise to a new solution for dynamic treatment regimes and beyond.

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Assigning optimal treatments to individual patients based on their characteristics is the ultimate goal of precision medicine. Deriving evidence-based recommendations from observational data while considering the causal treatment effects and patient heterogeneity is a challenging task, especially in situations of multiple treatment options. Herein, we propose a reference-free R-learner based on a simplex algorithm for treatment recommendation. We showed through extensive simulation that the proposed method produced accurate recommendations that corresponded to optimal treatment outcomes, regardless of the reference group. We used the method to analyze data from the Systolic Blood Pressure Intervention Trial (SPRINT) and achieved recommendations consistent with the current clinical guidelines.

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Objective: To compare the performance of a deep learning survival network with the tumor, node, and metastasis (TNM) staging system in survival prediction and test the reliability of individual treatment recommendations provided by the network. Methods: In this population-based cohort study, we developed and validated a deep learning survival model using consecutive cases of newly diagnosed stage I to IV esophageal cancer between January 2004 and December 2015 in a Surveillance, Epidemiology, and End Results (SEER) database. The model was externally validated in an independent cohort from Fujian Provincial Hospital. The C statistic was used to compare the performance of the deep learning survival model and TNM staging system. Two other deep learning risk prediction models were trained for treatment recommendations. A Kaplan-Meier survival curve was used to compare survival between the population that followed the recommended therapy and those who did not. Results: A total of 9069 patients were included in this study. The deep learning network showed more promising results in predicting esophageal cancer-specific survival than the TNM stage in the internal test dataset (C-index=0.753 vs. 0.638) and external validation dataset (C-index=0.687 vs. 0.643). The population who received the recommended treatments had superior survival compared to those who did not, based on the internal test dataset (hazard ratio, 0.753; 95% CI, 0.556-0.987; P=0.042) and the external validation dataset (hazard ratio, 0.633; 95% CI, 0.459-0.834; P=0.0003). Conclusion: Deep learning neural networks have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with esophageal cancer.

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Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model's recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications.

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BACKGROUND: GPs frequently prescribe antidepressants in mild depression. The aim of this study was to examine, how often Swiss GPs recommend antidepressants in various clinical presentations of mild depression and which factors contribute to antidepressant treatment recommendations. METHODS: We conducted an online survey among Swiss GPs with within-subject effect analysis. Alternating case vignettes described a typical female case of mild depression according to International Classification of Diseases, 10th edition criteria, with and without anxiety symptoms and sleep problems. GPs indicated for each vignette their preferred treatments (several recommendations were possible). Additionally, we assessed GP characteristics, attitudes towards depression treatments, and elements of clinical decision-making. RESULTS: Altogether 178 GPs completed the survey. In the initial description of a case with mild depression, 11% (95%-CI: 7%-17%) of GPs recommended antidepressants. If anxiety symptoms were added to the same case, 29% (23%-36%) recommended antidepressants. If sleep problems were mentioned, 47% (40%-55%) recommended antidepressants, and if both sleep problems and anxiety symptoms were mentioned, 63% (56%-70%) recommended antidepressants. Several factors were independently associated with increased odds of recommending antidepressants, specifically more years of practical experience, an advanced training in psychosomatic and psychosocial medicine, self-dispensation, and a higher perceived effectiveness of antidepressants. By contrast, a higher perceived influence of patient characteristics and the use of clinical practice guidelines were associated with reduced odds of recommending antidepressants. CONCLUSIONS: Consistent with depression practice guidelines, Swiss GPs rarely recommended antidepressants in mild depression if no co-indications (i.e., sleep problems and anxiety symptoms) were depicted. However, presence of sleep problems and anxiety symptoms, many years of practical experience, overestimation of antidepressants' effectiveness, self-dispensation, an advanced training in psychosomatic and psychosocial medicine, and non-use of clinical practice guidelines may independently lead to antidepressant over-prescribing.

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Digitalization of health care and the availability of suitable end devices lead to an increase in the use of telehealth applications. Most research on telehealth focuses on patients or organizations (like hospitals), while the role of physicians in this context is often neglected. In case of serious and chronic diseases, they play two major roles in the use of telehealth. Firstly, they may influence the patient's decision whether to use it at all (if more than one option is available, they may also influence the patient's choice of software). Secondly, if there is a need for a physicians' participation (eg, in telecare), an adoption decision by the physician to use the system is necessary. We develop a model to understand a physician's motivations to recommend the use of telehealth software to patients and to adopt it himself. The results demonstrate that physicians recommend telehealth based on their own use intention and the perceived performance improvements in patient treatment. Further, their own use intention is dependent on the usefulness of the system for their work. Potential disadvantages like decreased patient autonomy or cost of the system use do not influence the physician's decisions.

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Multiple sclerosis is a complex, autoimmune-mediated disease of the central nervous system characterized by inflammatory demyelination and axonal/neuronal damage. The approval of various disease-modifying therapies and our increased understanding of disease mechanisms and evolution in recent years have significantly changed the prognosis and course of the disease. This update of the Multiple Sclerosis Therapy Consensus Group treatment recommendation focuses on the most important recommendations for disease-modifying therapies of multiple sclerosis in 2021. Our recommendations are based on current scientific evidence and apply to those medications approved in wide parts of Europe, particularly German-speaking countries (Germany, Austria, and Switzerland).

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Multiple sclerosis is a complex, autoimmune-mediated disease of the central nervous system characterized by inflammatory demyelination and axonal/neuronal damage. The approval of various disease-modifying therapies and our increased understanding of disease mechanisms and evolution in recent years have significantly changed the prognosis and course of the disease. This update of the Multiple Sclerosis Therapy Consensus Group treatment recommendation focuses on the most important recommendations for disease-modifying therapies of multiple sclerosis in 2021. Our recommendations are based on current scientific evidence and apply to those medications approved in wide parts of Europe, particularly German-speaking countries (Germany, Austria, Switzerland).

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